PGS Educational Attainment
Import Data and create functions
Create functions
Here, we create functions for:
- Calculating cut-off values using either mean + 2SD or mean - 2SD
- Calculating whether individuals recover from a drop in a test
- Fit a cox regression based on thirtile_years or thirtile_PGS
- Plot the survival curve for the cox regression
- Test model assumptions for the cox regression
Survival Analysis
Using the ntile function from dplyr, the lower tertile will be assigned value 1 (~ negative residual), middle tertile value 2 and upper tertile value 3 (~positive residual). The time-point is limited to the 9th follow-up (i.e., 48 months).
Mini-Mental State Examination (MMSE)
“The mini–mental state examination (MMSE) is a 30-point questionnaire that is used extensively in clinical and research settings to measure cognitive impairment. It is commonly used in medicine and allied health to screen for dementia. It is also used to estimate the severity and progression of cognitive impairment and to follow the course of cognitive changes in an individual over time; thus making it an effective way to document an individual’s response to treatment.Administration of the test takes between 5 and 10 minutes and examines functions including registration (repeating named prompts), attention and calculation, recall, language, ability to follow simple commands and orientation. […] Any score of 24 or more (out of 30) indicates a normal cognition. Below this, scores can indicate severe (≤9 points), moderate (10–18 points) or mild (19-23 points) cognitive impairment.” (Wikipedia.org). The MMSE scores were normalized using the NormPsy package and then the cut-off was calculated.
Boxplots of MMSE by Age Group at Baseline
To see if it is necessary to stratify for age groups effect of polygenic risk score for EA and age group was tested using linear regression. The results are displayed below.
##
## Call:
## lm(formula = MMSE ~ EA22 + Age_Group, data = long_dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -27.415 -1.189 1.109 2.458 3.473
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 27.12540 0.16657 162.844 < 2e-16 ***
## EA22 0.45636 0.11384 4.009 6.19e-05 ***
## Age_Group -0.01018 0.06723 -0.151 0.88
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.642 on 4822 degrees of freedom
## (6151 observations deleted due to missingness)
## Multiple R-squared: 0.003326, Adjusted R-squared: 0.002912
## F-statistic: 8.045 on 2 and 4822 DF, p-value: 0.0003249##
## Call:
## lm(formula = MMSE_norm ~ EA22 + Age_Group, data = long_dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -77.457 -14.481 1.461 21.439 29.791
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 75.4231 0.9389 80.330 < 2e-16 ***
## EA22 3.7284 0.6416 5.811 6.62e-09 ***
## Age_Group -0.1925 0.3790 -0.508 0.611
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 20.53 on 4822 degrees of freedom
## (6151 observations deleted due to missingness)
## Multiple R-squared: 0.006955, Adjusted R-squared: 0.006543
## F-statistic: 16.89 on 2 and 4822 DF, p-value: 4.923e-08MMSE Survival Analysis
Next the survival analysis was conducted for the genetic capacity of educational attainment and the years of actual educational attainment.
Data Filtering
We created “age” as a time-dependent variable as well as “tstart” and “tstop”.
Descriptive Statistics
The following output shows:
- the number of individuals overall
- the number of unique individuals in the analysis
- the number of males/females in the analysis
- the number of individuals per time-point
## [1] 1408## [1] 713## # A tibble: 2 × 2
## PTGENDER Count
## <fct> <int>
## 1 Male 391
## 2 Female 322## # A tibble: 10 × 2
## VISCODE Count
## <dbl> <int>
## 1 1 713
## 2 2 361
## 3 3 671
## 4 4 611
## 5 5 534
## 6 6 575
## 7 7 331
## 8 8 385
## 9 9 122
## 10 10 334From now on, the reference for the regression model is: * “Medium PGS” for thirtile_PGS * “Medium EA” for thirtile_years
Here is the output for the Cox Proportional Hazards model of MMSE
## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_PGS +
## age + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 4637, number of events= 1316
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_PGSHigh PGS -0.184464 0.831550 0.070911 -2.601 0.00929 **
## thirtile_PGSLow PGS 0.087915 1.091895 0.064726 1.358 0.17438
## age 0.004181 1.004190 0.009293 0.450 0.65276
## PTGENDERFemale -1.454813 0.233444 0.986521 -1.475 0.14030
## age:PTGENDERFemale 0.020399 1.020609 0.013861 1.472 0.14110
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_PGSHigh PGS 0.8315 1.2026 0.72365 0.9555
## thirtile_PGSLow PGS 1.0919 0.9158 0.96180 1.2396
## age 1.0042 0.9958 0.98607 1.0226
## PTGENDERFemale 0.2334 4.2837 0.03376 1.6140
## age:PTGENDERFemale 1.0206 0.9798 0.99326 1.0487
##
## Concordance= 0.541 (se = 0.009 )
## Likelihood ratio test= 21.07 on 5 df, p=8e-04
## Wald test = 20.6 on 5 df, p=0.001
## Score (logrank) test = 20.67 on 5 df, p=9e-04## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_years +
## age + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 4637, number of events= 1316
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_yearsLow EA 0.628016 1.873890 0.065323 9.614 < 2e-16 ***
## thirtile_yearsHigh EA -0.223310 0.799867 0.075401 -2.962 0.00306 **
## age -0.001728 0.998274 0.009222 -0.187 0.85138
## PTGENDERFemale -0.889013 0.411061 0.982166 -0.905 0.36538
## age:PTGENDERFemale 0.010675 1.010732 0.013807 0.773 0.43943
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_yearsLow EA 1.8739 0.5336 1.64870 2.1298
## thirtile_yearsHigh EA 0.7999 1.2502 0.68998 0.9273
## age 0.9983 1.0017 0.98039 1.0165
## PTGENDERFemale 0.4111 2.4327 0.05996 2.8179
## age:PTGENDERFemale 1.0107 0.9894 0.98375 1.0385
##
## Concordance= 0.617 (se = 0.009 )
## Likelihood ratio test= 173.8 on 5 df, p=<2e-16
## Wald test = 177.4 on 5 df, p=<2e-16
## Score (logrank) test = 185.3 on 5 df, p=<2e-16
Cox Assumptions
Alzheimer’s Disease Assessment Scale
The Cognitive Subscale Alzheimer’s Disease Assessment Scale (ADAS) is made of 11 tasks that include both subject-completed tests and observer-based assessments, assessing the memory, language, and praxis domains. The result is a global final score ranging from 0 to 70, based on the sum of the scores of the single tasks (ADAS11).
Beyond the ADAS11 score, the ADNI study included also an additional test of delayed word recall and a number cancellation or maze task, which are further summed to have a new total score that ranges from 0 to 85 (ADAS13).
In addition, the score of the task 4 (Word Recognition, ADASQ4) was included in the ADNIMERGE dataset.
ADAS11
## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_PGS +
## age + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 4637, number of events= 1414
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_PGSHigh PGS -0.063265 0.938694 0.066367 -0.953 0.340
## thirtile_PGSLow PGS -0.015172 0.984943 0.063791 -0.238 0.812
## age -0.003938 0.996070 0.008852 -0.445 0.656
## PTGENDERFemale -0.433339 0.648340 0.939287 -0.461 0.645
## age:PTGENDERFemale 0.005503 1.005518 0.013237 0.416 0.678
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_PGSHigh PGS 0.9387 1.0653 0.8242 1.069
## thirtile_PGSLow PGS 0.9849 1.0153 0.8692 1.116
## age 0.9961 1.0039 0.9789 1.014
## PTGENDERFemale 0.6483 1.5424 0.1029 4.086
## age:PTGENDERFemale 1.0055 0.9945 0.9798 1.032
##
## Concordance= 0.507 (se = 0.009 )
## Likelihood ratio test= 1.81 on 5 df, p=0.9
## Wald test = 1.81 on 5 df, p=0.9
## Score (logrank) test = 1.81 on 5 df, p=0.9## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_years +
## age + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 4637, number of events= 1414
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_yearsLow EA 0.497794 1.645088 0.063624 7.824 5.12e-15 ***
## thirtile_yearsHigh EA -0.144867 0.865137 0.070040 -2.068 0.0386 *
## age -0.008643 0.991394 0.008817 -0.980 0.3269
## PTGENDERFemale -0.042202 0.958676 0.936665 -0.045 0.9641
## age:PTGENDERFemale -0.001470 0.998531 0.013207 -0.111 0.9114
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_yearsLow EA 1.6451 0.6079 1.4522 1.8636
## thirtile_yearsHigh EA 0.8651 1.1559 0.7542 0.9924
## age 0.9914 1.0087 0.9744 1.0087
## PTGENDERFemale 0.9587 1.0431 0.1529 6.0112
## age:PTGENDERFemale 0.9985 1.0015 0.9730 1.0247
##
## Concordance= 0.583 (se = 0.009 )
## Likelihood ratio test= 105.9 on 5 df, p=<2e-16
## Wald test = 109 on 5 df, p=<2e-16
## Score (logrank) test = 111.5 on 5 df, p=<2e-16
Cox Assumptions
ADAS13
“The ADAS13 was included as a global measure of cognitive function. ADAS13 is a test battery developed to assess severity of cognitive impairment associated with AD and includes subtests and clinical evaluations assessing memory function, reasoning, language function, orientation and praxis. The ADAS13 is a modified version of the original ADAS-Cog-11, adding a cancellation task and a delayed free recall task. The higher the scores, the more severe impairment of cognitive function.” (Mofrad et al., 2021)
## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_PGS +
## age + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 4629, number of events= 1492
## (8 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_PGSHigh PGS -0.0846691 0.9188163 0.0646370 -1.310 0.190
## thirtile_PGSLow PGS -0.0206868 0.9795257 0.0619498 -0.334 0.738
## age 0.0144384 1.0145431 0.0087813 1.644 0.100
## PTGENDERFemale -0.0045916 0.9954189 0.9302916 -0.005 0.996
## age:PTGENDERFemale -0.0004925 0.9995077 0.0130755 -0.038 0.970
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_PGSHigh PGS 0.9188 1.0884 0.8095 1.043
## thirtile_PGSLow PGS 0.9795 1.0209 0.8675 1.106
## age 1.0145 0.9857 0.9972 1.032
## PTGENDERFemale 0.9954 1.0046 0.1607 6.164
## age:PTGENDERFemale 0.9995 1.0005 0.9742 1.025
##
## Concordance= 0.517 (se = 0.009 )
## Likelihood ratio test= 7.14 on 5 df, p=0.2
## Wald test = 7.06 on 5 df, p=0.2
## Score (logrank) test = 7.07 on 5 df, p=0.2## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_years +
## age + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 4629, number of events= 1492
## (8 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_yearsLow EA 0.478914 1.614320 0.061782 7.752 9.07e-15 ***
## thirtile_yearsHigh EA -0.165474 0.847492 0.068219 -2.426 0.0153 *
## age 0.009846 1.009895 0.008748 1.126 0.2604
## PTGENDERFemale 0.409385 1.505891 0.927855 0.441 0.6591
## age:PTGENDERFemale -0.007762 0.992268 0.013049 -0.595 0.5519
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_yearsLow EA 1.6143 0.6195 1.4302 1.8221
## thirtile_yearsHigh EA 0.8475 1.1800 0.7414 0.9687
## age 1.0099 0.9902 0.9927 1.0274
## PTGENDERFemale 1.5059 0.6641 0.2443 9.2808
## age:PTGENDERFemale 0.9923 1.0078 0.9672 1.0180
##
## Concordance= 0.595 (se = 0.009 )
## Likelihood ratio test= 113.9 on 5 df, p=<2e-16
## Wald test = 116.7 on 5 df, p=<2e-16
## Score (logrank) test = 119.4 on 5 df, p=<2e-16
Cox Assumptions
ADASQ4
## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_PGS +
## age + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 4637, number of events= 1477
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_PGSHigh PGS -0.034160 0.966417 0.065642 -0.520 0.6028
## thirtile_PGSLow PGS 0.080771 1.084123 0.062340 1.296 0.1951
## age 0.019442 1.019633 0.008839 2.200 0.0278 *
## PTGENDERFemale 0.217219 1.242616 0.936621 0.232 0.8166
## age:PTGENDERFemale -0.003948 0.996060 0.013160 -0.300 0.7642
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_PGSHigh PGS 0.9664 1.0347 0.8497 1.099
## thirtile_PGSLow PGS 1.0841 0.9224 0.9594 1.225
## age 1.0196 0.9807 1.0021 1.037
## PTGENDERFemale 1.2426 0.8048 0.1982 7.791
## age:PTGENDERFemale 0.9961 1.0040 0.9707 1.022
##
## Concordance= 0.527 (se = 0.009 )
## Likelihood ratio test= 11.8 on 5 df, p=0.04
## Wald test = 11.76 on 5 df, p=0.04
## Score (logrank) test = 11.78 on 5 df, p=0.04## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_years +
## age + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 4637, number of events= 1477
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_yearsLow EA 0.40637 1.50136 0.06180 6.575 4.86e-11 ***
## thirtile_yearsHigh EA -0.21104 0.80974 0.06806 -3.101 0.00193 **
## age 0.01541 1.01553 0.00883 1.745 0.08092 .
## PTGENDERFemale 0.63147 1.88037 0.93560 0.675 0.49972
## age:PTGENDERFemale -0.01106 0.98901 0.01315 -0.841 0.40061
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_yearsLow EA 1.5014 0.6661 1.3301 1.6947
## thirtile_yearsHigh EA 0.8097 1.2350 0.7086 0.9253
## age 1.0155 0.9847 0.9981 1.0333
## PTGENDERFemale 1.8804 0.5318 0.3005 11.7660
## age:PTGENDERFemale 0.9890 1.0111 0.9638 1.0148
##
## Concordance= 0.589 (se = 0.009 )
## Likelihood ratio test= 101 on 5 df, p=<2e-16
## Wald test = 102.2 on 5 df, p=<2e-16
## Score (logrank) test = 104.2 on 5 df, p=<2e-16
Cox Assumptions
CDRSB
“The clinical dementia rating (CDR) scale is commonly used to diagnose dementia due to Alzheimer’s disease (AD). The sum of boxes of the CDR (CDR-SB) has recently been emphasized and applied to interventional trials for tracing the progression of cognitive impairment (CI) in the early stages of AD.” (Tzeng et al., 2022)
See Table 3 for explanation on the staging category (O’Bryant et al., 2012)
## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_PGS +
## age + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 4637, number of events= 3258
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_PGSHigh PGS -0.094731 0.909618 0.044650 -2.122 0.03387 *
## thirtile_PGSLow PGS 0.139599 1.149813 0.041805 3.339 0.00084 ***
## age -0.044252 0.956713 0.005662 -7.816 5.47e-15 ***
## PTGENDERFemale 1.660855 5.263808 0.609315 2.726 0.00642 **
## age:PTGENDERFemale -0.026597 0.973753 0.008660 -3.071 0.00213 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_PGSHigh PGS 0.9096 1.0994 0.8334 0.9928
## thirtile_PGSLow PGS 1.1498 0.8697 1.0594 1.2480
## age 0.9567 1.0452 0.9462 0.9674
## PTGENDERFemale 5.2638 0.1900 1.5946 17.3760
## age:PTGENDERFemale 0.9738 1.0270 0.9574 0.9904
##
## Concordance= 0.616 (se = 0.008 )
## Likelihood ratio test= 212.6 on 5 df, p=<2e-16
## Wald test = 212.6 on 5 df, p=<2e-16
## Score (logrank) test = 215.1 on 5 df, p=<2e-16## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_years +
## age + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 4637, number of events= 3258
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_yearsLow EA 0.354099 1.424896 0.043354 8.168 3.14e-16 ***
## thirtile_yearsHigh EA 0.043096 1.044038 0.043479 0.991 0.321596
## age -0.046554 0.954513 0.005650 -8.240 < 2e-16 ***
## PTGENDERFemale 1.852772 6.377472 0.607142 3.052 0.002276 **
## age:PTGENDERFemale -0.029934 0.970509 0.008634 -3.467 0.000526 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_yearsLow EA 1.4249 0.7018 1.3088 1.5513
## thirtile_yearsHigh EA 1.0440 0.9578 0.9588 1.1369
## age 0.9545 1.0477 0.9440 0.9651
## PTGENDERFemale 6.3775 0.1568 1.9402 20.9628
## age:PTGENDERFemale 0.9705 1.0304 0.9542 0.9871
##
## Concordance= 0.629 (se = 0.008 )
## Likelihood ratio test= 259.2 on 5 df, p=<2e-16
## Wald test = 261.6 on 5 df, p=<2e-16
## Score (logrank) test = 263.8 on 5 df, p=<2e-16
DIGITSCORE
“The DSST (Digit Symbol Substitution Test) is a paper-and-pencil cognitive test presented on a single sheet of paper that requires a subject to match symbols to numbers according to a key located on the top of the page. The subject copies the symbol into spaces below a row of numbers. The number of correct symbols within the allowed time, usually 90 to 120 seconds, constitutes the score.” (Jaeger, 2018) The lower the scores, the more severe impairment of cognitive function.
## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_PGS +
## age + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 2001, number of events= 373
## (2636 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_PGSHigh PGS -0.032671 0.967857 0.132365 -0.247 0.805044
## thirtile_PGSLow PGS 0.112431 1.118995 0.124207 0.905 0.365364
## age -0.119014 0.887795 0.016075 -7.403 1.33e-13 ***
## PTGENDERFemale -6.993365 0.000918 1.823897 -3.834 0.000126 ***
## age:PTGENDERFemale 0.099553 1.104677 0.025648 3.882 0.000104 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_PGSHigh PGS 0.967857 1.0332 7.467e-01 1.25453
## thirtile_PGSLow PGS 1.118995 0.8937 8.772e-01 1.42743
## age 0.887795 1.1264 8.603e-01 0.91621
## PTGENDERFemale 0.000918 1089.3806 2.572e-05 0.03276
## age:PTGENDERFemale 1.104677 0.9052 1.051e+00 1.16163
##
## Concordance= 0.641 (se = 0.016 )
## Likelihood ratio test= 53.09 on 5 df, p=3e-10
## Wald test = 56.58 on 5 df, p=6e-11
## Score (logrank) test = 58.2 on 5 df, p=3e-11## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_years +
## age + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 2001, number of events= 373
## (2636 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_yearsLow EA 0.892128 2.440316 0.142203 6.274 3.53e-10 ***
## thirtile_yearsHigh EA 0.332456 1.394389 0.157938 2.105 0.03529 *
## age -0.107240 0.898310 0.016234 -6.606 3.95e-11 ***
## PTGENDERFemale -5.935336 0.002644 1.807956 -3.283 0.00103 **
## age:PTGENDERFemale 0.082990 1.086531 0.025439 3.262 0.00111 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_yearsLow EA 2.440316 0.4098 1.847e+00 3.22470
## thirtile_yearsHigh EA 1.394389 0.7172 1.023e+00 1.90029
## age 0.898310 1.1132 8.702e-01 0.92735
## PTGENDERFemale 0.002644 378.1670 7.645e-05 0.09146
## age:PTGENDERFemale 1.086531 0.9204 1.034e+00 1.14208
##
## Concordance= 0.677 (se = 0.015 )
## Likelihood ratio test= 99.96 on 5 df, p=<2e-16
## Wald test = 100.7 on 5 df, p=<2e-16
## Score (logrank) test = 106 on 5 df, p=<2e-16
FAQ
The Functional Activities Questionnaire is used to assess an individual’s functional abilities in daily living activities. It is a caregiver-based questionnaire that helps evaluate how well a person is able to perform various instrumental activities of daily living (IADLs) and basic activities of daily living (ADLs). (ChatGPT) Sum scores (range 0-30). The score range for each item is 0–3 (higher scores indicate greater impairment; 0 = normal or never did but could do now; 1 = has difficulty but does by self or never did but would have difficulty now; 2 = requires assistance; 3 = dependent). There is no established cut-off score for IADL impairment on the FAQ. However, one study reported that a total FAQ score (sum of all 10 item scores; range 0–30) of ≥ 6 is suggestive of functional impairment [ 20]. (Marshall et al., 2015)
## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_PGS +
## age + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 4634, number of events= 1219
## (3 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_PGSHigh PGS -0.050695 0.950569 0.074284 -0.682 0.494957
## thirtile_PGSLow PGS 0.244962 1.277573 0.068151 3.594 0.000325 ***
## age 0.001186 1.001187 0.009088 0.130 0.896176
## PTGENDERFemale -1.839186 0.158947 1.055453 -1.743 0.081411 .
## age:PTGENDERFemale 0.020929 1.021150 0.014835 1.411 0.158313
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_PGSHigh PGS 0.9506 1.0520 0.82178 1.100
## thirtile_PGSLow PGS 1.2776 0.7827 1.11783 1.460
## age 1.0012 0.9988 0.98351 1.019
## PTGENDERFemale 0.1589 6.2914 0.02008 1.258
## age:PTGENDERFemale 1.0211 0.9793 0.99189 1.051
##
## Concordance= 0.564 (se = 0.01 )
## Likelihood ratio test= 58.74 on 5 df, p=2e-11
## Wald test = 57.52 on 5 df, p=4e-11
## Score (logrank) test = 58.04 on 5 df, p=3e-11## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_years +
## age + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 4634, number of events= 1219
## (3 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_yearsLow EA 0.449071 1.566855 0.068461 6.560 5.4e-11 ***
## thirtile_yearsHigh EA -0.152468 0.858586 0.074419 -2.049 0.0405 *
## age -0.002850 0.997154 0.009094 -0.313 0.7540
## PTGENDERFemale -1.251560 0.286058 1.056241 -1.185 0.2360
## age:PTGENDERFemale 0.011507 1.011574 0.014856 0.775 0.4386
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_yearsLow EA 1.5669 0.6382 1.37011 1.7919
## thirtile_yearsHigh EA 0.8586 1.1647 0.74206 0.9934
## age 0.9972 1.0029 0.97954 1.0151
## PTGENDERFemale 0.2861 3.4958 0.03609 2.2674
## age:PTGENDERFemale 1.0116 0.9886 0.98254 1.0415
##
## Concordance= 0.593 (se = 0.009 )
## Likelihood ratio test= 114.5 on 5 df, p=<2e-16
## Wald test = 114.6 on 5 df, p=<2e-16
## Score (logrank) test = 116 on 5 df, p=<2e-16
LDELTOTAL
## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_PGS +
## age + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 4637, number of events= 1793
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_PGSHigh PGS -0.086751 0.916905 0.061117 -1.419 0.155776
## thirtile_PGSLow PGS 0.212335 1.236562 0.056025 3.790 0.000151 ***
## age -0.001179 0.998822 0.007839 -0.150 0.880463
## PTGENDERFemale -0.420322 0.656835 0.839097 -0.501 0.616426
## age:PTGENDERFemale 0.004446 1.004456 0.011827 0.376 0.706967
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_PGSHigh PGS 0.9169 1.0906 0.8134 1.034
## thirtile_PGSLow PGS 1.2366 0.8087 1.1080 1.380
## age 0.9988 1.0012 0.9836 1.014
## PTGENDERFemale 0.6568 1.5225 0.1268 3.402
## age:PTGENDERFemale 1.0045 0.9956 0.9814 1.028
##
## Concordance= 0.549 (se = 0.009 )
## Likelihood ratio test= 32.88 on 5 df, p=4e-06
## Wald test = 33.16 on 5 df, p=3e-06
## Score (logrank) test = 33.32 on 5 df, p=3e-06## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_years +
## age + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 4637, number of events= 1793
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_yearsLow EA 0.675806 1.965617 0.056535 11.954 <2e-16 ***
## thirtile_yearsHigh EA -0.158599 0.853339 0.063626 -2.493 0.0127 *
## age -0.006271 0.993749 0.007793 -0.805 0.4210
## PTGENDERFemale 0.341131 1.406537 0.837320 0.407 0.6837
## age:PTGENDERFemale -0.008034 0.991998 0.011810 -0.680 0.4963
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_yearsLow EA 1.9656 0.5087 1.7594 2.1959
## thirtile_yearsHigh EA 0.8533 1.1719 0.7533 0.9667
## age 0.9937 1.0063 0.9787 1.0090
## PTGENDERFemale 1.4065 0.7110 0.2725 7.2590
## age:PTGENDERFemale 0.9920 1.0081 0.9693 1.0152
##
## Concordance= 0.623 (se = 0.009 )
## Likelihood ratio test= 236.9 on 5 df, p=<2e-16
## Wald test = 245.3 on 5 df, p=<2e-16
## Score (logrank) test = 254.7 on 5 df, p=<2e-16
MOCA
Reference literature: doi: 10.1111/j.1532-5415.2005.53221.x
## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_PGS +
## age + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 2682, number of events= 1669
## (1955 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_PGSHigh PGS -0.130146 0.877967 0.062307 -2.089 0.0367 *
## thirtile_PGSLow PGS 0.127501 1.135986 0.058106 2.194 0.0282 *
## age 0.022361 1.022613 0.007985 2.800 0.0051 **
## PTGENDERFemale -0.133812 0.874755 0.873449 -0.153 0.8782
## age:PTGENDERFemale -0.003673 0.996334 0.012398 -0.296 0.7670
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_PGSHigh PGS 0.8780 1.1390 0.7770 0.992
## thirtile_PGSLow PGS 1.1360 0.8803 1.0137 1.273
## age 1.0226 0.9779 1.0067 1.039
## PTGENDERFemale 0.8748 1.1432 0.1579 4.846
## age:PTGENDERFemale 0.9963 1.0037 0.9724 1.021
##
## Concordance= 0.604 (se = 0.011 )
## Likelihood ratio test= 93.01 on 5 df, p=<2e-16
## Wald test = 91.69 on 5 df, p=<2e-16
## Score (logrank) test = 92.88 on 5 df, p=<2e-16## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_years +
## age + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 2682, number of events= 1669
## (1955 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_yearsLow EA 0.358667 1.431420 0.060403 5.938 2.89e-09 ***
## thirtile_yearsHigh EA -0.083251 0.920120 0.059807 -1.392 0.1639
## age 0.018816 1.018995 0.007965 2.362 0.0182 *
## PTGENDERFemale 0.169700 1.184949 0.874779 0.194 0.8462
## age:PTGENDERFemale -0.008695 0.991343 0.012422 -0.700 0.4840
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_yearsLow EA 1.4314 0.6986 1.2716 1.611
## thirtile_yearsHigh EA 0.9201 1.0868 0.8183 1.035
## age 1.0190 0.9814 1.0032 1.035
## PTGENDERFemale 1.1849 0.8439 0.2133 6.581
## age:PTGENDERFemale 0.9913 1.0087 0.9675 1.016
##
## Concordance= 0.62 (se = 0.011 )
## Likelihood ratio test= 130.4 on 5 df, p=<2e-16
## Wald test = 130.6 on 5 df, p=<2e-16
## Score (logrank) test = 132.1 on 5 df, p=<2e-16
Rey-Auditory Verbal Learning Test (RAVLT)
The RAVLT was included as a measure of memory function. In this test, the participants are asked to recall words from a list of 15 nouns immediately after each of five learning trials and after a short and a long delay. Two measures known to be sensitive to cognitive changes in patients with AD were included in the present study: Immediate recall (RAVLT-Im): the number of correct responses across the immediate recall of the five learning trials; percent forgetting (RAVLT-PF): the score on the fifth learning trial minus the score on the long delayed recall, divided by the score obtained on the fifth learning trial. The lower the scores, the more severe impairment of cognitive function.
Different summary scores are derived from raw RAVLT scores. These include RAVLT Immediate (the sum of scores from 5 first trials (Trials 1 to 5)), RAVLT Learning (the score of Trial 5 minus the score of Trial 1), RAVLT Forgetting (the score of Trial 5 minus score of the delayed recall) and RAVLT Percent Forgetting (RAVLT Forgetting divided by the score of Trial 5). We use naming of the ADNI merge table3 for these summary measures. We investigated the relationship between MRI measures and RAVLT cognitive test scores by estimating the RAVLT Immediate and RAVLT Percent Forgetting from the gray matter density. These two summary scores were selected since they highlight different aspects of episodic memory, learning (RAVLT Immediate) and delayed memory (RAVLT Percent forgetting), essential to AD and previous studies (Estévez-González et al., 2003, Wang et al., 2011, Gomar et al., 2014, Moradi et al., 2015) have indicated strong relationships between these two RAVLT measures and Alzheimer’s disease.
RAVLT Immediate
## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_PGS +
## age + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 4637, number of events= 912
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_PGSHigh PGS -0.12253 0.88468 0.08692 -1.410 0.158634
## thirtile_PGSLow PGS 0.25983 1.29671 0.07805 3.329 0.000871 ***
## age 0.01766 1.01782 0.01082 1.632 0.102696
## PTGENDERFemale -2.31028 0.09923 1.25028 -1.848 0.064631 .
## age:PTGENDERFemale 0.02805 1.02845 0.01752 1.601 0.109357
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_PGSHigh PGS 0.88468 1.1304 0.746096 1.049
## thirtile_PGSLow PGS 1.29671 0.7712 1.112780 1.511
## age 1.01782 0.9825 0.996456 1.040
## PTGENDERFemale 0.09923 10.0772 0.008559 1.151
## age:PTGENDERFemale 1.02845 0.9723 0.993732 1.064
##
## Concordance= 0.58 (se = 0.011 )
## Likelihood ratio test= 56.05 on 5 df, p=8e-11
## Wald test = 53.92 on 5 df, p=2e-10
## Score (logrank) test = 54.54 on 5 df, p=2e-10## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_years +
## age + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 4637, number of events= 912
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_yearsLow EA 0.597680 1.817896 0.078386 7.625 2.44e-14 ***
## thirtile_yearsHigh EA -0.224044 0.799280 0.089784 -2.495 0.0126 *
## age 0.009728 1.009776 0.010798 0.901 0.3676
## PTGENDERFemale -1.860228 0.155637 1.248172 -1.490 0.1361
## age:PTGENDERFemale 0.020041 1.020243 0.017497 1.145 0.2521
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_yearsLow EA 1.8179 0.5501 1.55900 2.1198
## thirtile_yearsHigh EA 0.7993 1.2511 0.67031 0.9531
## age 1.0098 0.9903 0.98863 1.0314
## PTGENDERFemale 0.1556 6.4252 0.01348 1.7970
## age:PTGENDERFemale 1.0202 0.9802 0.98585 1.0558
##
## Concordance= 0.62 (se = 0.01 )
## Likelihood ratio test= 139.8 on 5 df, p=<2e-16
## Wald test = 139.2 on 5 df, p=<2e-16
## Score (logrank) test = 143 on 5 df, p=<2e-16
RAVLT Percentage Forgetting
## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_PGS +
## age + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 4622, number of events= 1293
## (15 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_PGSHigh PGS -0.078870 0.924160 0.070475 -1.119 0.263
## thirtile_PGSLow PGS 0.072515 1.075209 0.066314 1.094 0.274
## age -0.008053 0.991979 0.009238 -0.872 0.383
## PTGENDERFemale -0.720354 0.486580 0.978940 -0.736 0.462
## age:PTGENDERFemale 0.009712 1.009759 0.013796 0.704 0.481
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_PGSHigh PGS 0.9242 1.0821 0.80493 1.061
## thirtile_PGSLow PGS 1.0752 0.9301 0.94416 1.224
## age 0.9920 1.0081 0.97418 1.010
## PTGENDERFemale 0.4866 2.0552 0.07143 3.315
## age:PTGENDERFemale 1.0098 0.9903 0.98282 1.037
##
## Concordance= 0.521 (se = 0.01 )
## Likelihood ratio test= 5.94 on 5 df, p=0.3
## Wald test = 5.94 on 5 df, p=0.3
## Score (logrank) test = 5.95 on 5 df, p=0.3## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_years +
## age + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 4622, number of events= 1293
## (15 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_yearsLow EA 0.338505 1.402849 0.066847 5.064 4.11e-07 ***
## thirtile_yearsHigh EA -0.122614 0.884605 0.071194 -1.722 0.085 .
## age -0.011156 0.988906 0.009207 -1.212 0.226
## PTGENDERFemale -0.376762 0.686079 0.977079 -0.386 0.700
## age:PTGENDERFemale 0.003945 1.003953 0.013777 0.286 0.775
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_yearsLow EA 1.4028 0.7128 1.2306 1.599
## thirtile_yearsHigh EA 0.8846 1.1304 0.7694 1.017
## age 0.9889 1.0112 0.9712 1.007
## PTGENDERFemale 0.6861 1.4576 0.1011 4.657
## age:PTGENDERFemale 1.0040 0.9961 0.9772 1.031
##
## Concordance= 0.562 (se = 0.01 )
## Likelihood ratio test= 48.54 on 5 df, p=3e-09
## Wald test = 49.66 on 5 df, p=2e-09
## Score (logrank) test = 50.22 on 5 df, p=1e-09
RAVLT Forgetting
## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_PGS +
## age + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 4637, number of events= 132
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_PGSHigh PGS -6.845e-01 5.043e-01 2.290e-01 -2.989 0.002797 **
## thirtile_PGSLow PGS -4.574e-01 6.329e-01 1.994e-01 -2.295 0.021757 *
## age -1.841e-01 8.319e-01 3.231e-02 -5.698 1.22e-08 ***
## PTGENDERFemale -1.008e+01 4.193e-05 2.886e+00 -3.493 0.000477 ***
## age:PTGENDERFemale 1.549e-01 1.168e+00 4.169e-02 3.715 0.000203 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_PGSHigh PGS 5.043e-01 1.983e+00 3.219e-01 0.79002
## thirtile_PGSLow PGS 6.329e-01 1.580e+00 4.282e-01 0.93547
## age 8.319e-01 1.202e+00 7.808e-01 0.88624
## PTGENDERFemale 4.193e-05 2.385e+04 1.467e-07 0.01199
## age:PTGENDERFemale 1.168e+00 8.565e-01 1.076e+00 1.26692
##
## Concordance= 0.691 (se = 0.023 )
## Likelihood ratio test= 59.48 on 5 df, p=2e-11
## Wald test = 47.98 on 5 df, p=4e-09
## Score (logrank) test = 52.76 on 5 df, p=4e-10## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_years +
## age + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 4637, number of events= 132
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_yearsLow EA -3.038e-01 7.380e-01 2.189e-01 -1.388 0.165178
## thirtile_yearsHigh EA -8.133e-02 9.219e-01 2.066e-01 -0.394 0.693802
## age -1.814e-01 8.341e-01 3.226e-02 -5.623 1.87e-08 ***
## PTGENDERFemale -1.037e+01 3.142e-05 2.889e+00 -3.589 0.000333 ***
## age:PTGENDERFemale 1.595e-01 1.173e+00 4.177e-02 3.819 0.000134 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_yearsLow EA 7.380e-01 1.355e+00 4.805e-01 1.133412
## thirtile_yearsHigh EA 9.219e-01 1.085e+00 6.149e-01 1.382049
## age 8.341e-01 1.199e+00 7.830e-01 0.888519
## PTGENDERFemale 3.142e-05 3.183e+04 1.091e-07 0.009047
## age:PTGENDERFemale 1.173e+00 8.526e-01 1.081e+00 1.273009
##
## Concordance= 0.674 (se = 0.023 )
## Likelihood ratio test= 50.96 on 5 df, p=9e-10
## Wald test = 39.68 on 5 df, p=2e-07
## Score (logrank) test = 44.48 on 5 df, p=2e-08
RAVLT Learning
## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_PGS +
## age + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 4637, number of events= 65
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_PGSHigh PGS -0.149033 0.861540 0.335680 -0.444 0.657
## thirtile_PGSLow PGS 0.263902 1.302001 0.292128 0.903 0.366
## age -0.009928 0.990122 0.047196 -0.210 0.833
## PTGENDERFemale 4.543326 94.002896 4.256684 1.067 0.286
## age:PTGENDERFemale -0.056481 0.945085 0.060035 -0.941 0.347
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_PGSHigh PGS 0.8615 1.16071 0.44621 1.663e+00
## thirtile_PGSLow PGS 1.3020 0.76805 0.73443 2.308e+00
## age 0.9901 1.00998 0.90264 1.086e+00
## PTGENDERFemale 94.0029 0.01064 0.02238 3.949e+05
## age:PTGENDERFemale 0.9451 1.05811 0.84017 1.063e+00
##
## Concordance= 0.604 (se = 0.036 )
## Likelihood ratio test= 10.77 on 5 df, p=0.06
## Wald test = 11.44 on 5 df, p=0.04
## Score (logrank) test = 11.97 on 5 df, p=0.04## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_years +
## age + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 4637, number of events= 65
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_yearsLow EA -0.08662 0.91703 0.30873 -0.281 0.779
## thirtile_yearsHigh EA -0.01951 0.98068 0.29945 -0.065 0.948
## age -0.01299 0.98709 0.04727 -0.275 0.783
## PTGENDERFemale 4.48502 88.67911 4.26198 1.052 0.293
## age:PTGENDERFemale -0.05522 0.94628 0.06014 -0.918 0.359
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_yearsLow EA 0.9170 1.09048 0.50071 1.679e+00
## thirtile_yearsHigh EA 0.9807 1.01970 0.54530 1.764e+00
## age 0.9871 1.01308 0.89975 1.083e+00
## PTGENDERFemale 88.6791 0.01128 0.02089 3.764e+05
## age:PTGENDERFemale 0.9463 1.05677 0.84107 1.065e+00
##
## Concordance= 0.578 (se = 0.039 )
## Likelihood ratio test= 8.92 on 5 df, p=0.1
## Wald test = 9.57 on 5 df, p=0.09
## Score (logrank) test = 10.07 on 5 df, p=0.07
TRABSCORE
The Trail Making Test is a neuropsychological test of visual attention and task switching. It has two parts, in which the subject is instructed to connect a set of 25 dots as quickly as possible while maintaining accuracy.
The test can provide information about visual search speed, scanning, speed of processing, mental flexibility, and executive functioning. It is sensitive to cognitive impairment associated with dementia, including Alzheimer’s disease. (ChatGPT)
Record the total number of seconds to complete Part B (Trails B), up to a maximum of 300 seconds. If the participant is not finished by 300 seconds, the score is 300.
## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_PGS +
## age + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 4570, number of events= 713
## (67 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_PGSHigh PGS -0.045460 0.955558 0.093837 -0.484 0.628
## thirtile_PGSLow PGS 0.013764 1.013859 0.089869 0.153 0.878
## age 0.009093 1.009135 0.012797 0.711 0.477
## PTGENDERFemale -2.026426 0.131806 1.365849 -1.484 0.138
## age:PTGENDERFemale 0.028459 1.028868 0.019157 1.486 0.137
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_PGSHigh PGS 0.9556 1.0465 0.795028 1.149
## thirtile_PGSLow PGS 1.0139 0.9863 0.850121 1.209
## age 1.0091 0.9909 0.984139 1.035
## PTGENDERFemale 0.1318 7.5869 0.009064 1.917
## age:PTGENDERFemale 1.0289 0.9719 0.990953 1.068
##
## Concordance= 0.53 (se = 0.012 )
## Likelihood ratio test= 7.32 on 5 df, p=0.2
## Wald test = 7.09 on 5 df, p=0.2
## Score (logrank) test = 7.1 on 5 df, p=0.2## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_years +
## age + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 4570, number of events= 713
## (67 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_yearsLow EA 0.992617 2.698288 0.097564 10.174 <2e-16 ***
## thirtile_yearsHigh EA 0.351493 1.421187 0.105866 3.320 0.0009 ***
## age 0.008053 1.008086 0.012718 0.633 0.5266
## PTGENDERFemale -1.414410 0.243069 1.359138 -1.041 0.2980
## age:PTGENDERFemale 0.017782 1.017941 0.019075 0.932 0.3512
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_yearsLow EA 2.6983 0.3706 2.22865 3.267
## thirtile_yearsHigh EA 1.4212 0.7036 1.15488 1.749
## age 1.0081 0.9920 0.98327 1.034
## PTGENDERFemale 0.2431 4.1141 0.01694 3.488
## age:PTGENDERFemale 1.0179 0.9824 0.98059 1.057
##
## Concordance= 0.627 (se = 0.012 )
## Likelihood ratio test= 125.5 on 5 df, p=<2e-16
## Wald test = 124.5 on 5 df, p=<2e-16
## Score (logrank) test = 131.6 on 5 df, p=<2e-16
Patient’s Everyday Cognition (EcogPt)
The original version of the ECog is an informant-based measure of cognitively-relevant everyday abilities comprised of 39 items, covering six cognitively-relevant domains: Everyday Memory, Everyday Language, Everyday Visuospatial Abilities, and Everyday Planning, Everyday Organization, and Everyday Divided Attention. Ratings are made on a four-point scale: 1 = better or no change compared to 10 years earlier, 2 = questionable/occasionally worse, 3 = consistently a little worse, 4 = consistently much worse. (Tomaszewski Farias et al., 2012)
EcogPt Everyday Divided Attention
## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_PGS +
## age + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 2690, number of events= 337
## (1947 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_PGSHigh PGS 0.35961 1.43277 0.13996 2.569 0.0102 *
## thirtile_PGSLow PGS 0.25582 1.29152 0.13884 1.843 0.0654 .
## age -0.02051 0.97970 0.01883 -1.089 0.2760
## PTGENDERFemale 8.48969 4864.35573 1.79840 4.721 2.35e-06 ***
## age:PTGENDERFemale -0.11947 0.88739 0.02592 -4.609 4.05e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_PGSHigh PGS 1.4328 0.6979481 1.0890 1.885e+00
## thirtile_PGSLow PGS 1.2915 0.7742821 0.9838 1.695e+00
## age 0.9797 1.0207241 0.9442 1.017e+00
## PTGENDERFemale 4864.3557 0.0002056 143.2934 1.651e+05
## age:PTGENDERFemale 0.8874 1.1269029 0.8434 9.336e-01
##
## Concordance= 0.632 (se = 0.016 )
## Likelihood ratio test= 72.61 on 5 df, p=3e-14
## Wald test = 78.96 on 5 df, p=1e-15
## Score (logrank) test = 81.65 on 5 df, p=4e-16## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_years +
## age + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 2690, number of events= 337
## (1947 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_yearsLow EA -0.02594 0.97439 0.14528 -0.179 0.8583
## thirtile_yearsHigh EA 0.20944 1.23298 0.12644 1.656 0.0976 .
## age -0.02381 0.97647 0.01884 -1.264 0.2063
## PTGENDERFemale 8.36218 4282.02423 1.80846 4.624 3.77e-06 ***
## age:PTGENDERFemale -0.11752 0.88912 0.02607 -4.508 6.54e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_yearsLow EA 0.9744 1.0262800 0.7329 1.295e+00
## thirtile_yearsHigh EA 1.2330 0.8110422 0.9623 1.580e+00
## age 0.9765 1.0240976 0.9411 1.013e+00
## PTGENDERFemale 4282.0242 0.0002335 123.6763 1.483e+05
## age:PTGENDERFemale 0.8891 1.1247050 0.8448 9.357e-01
##
## Concordance= 0.635 (se = 0.016 )
## Likelihood ratio test= 69.53 on 5 df, p=1e-13
## Wald test = 75.91 on 5 df, p=6e-15
## Score (logrank) test = 78.82 on 5 df, p=1e-15
EcogPt Everyday Language
## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_PGS +
## age + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 2694, number of events= 368
## (1943 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_PGSHigh PGS -0.01586 0.98426 0.13576 -0.117 0.9070
## thirtile_PGSLow PGS 0.21647 1.24169 0.12521 1.729 0.0838 .
## age -0.02472 0.97559 0.01691 -1.462 0.1438
## PTGENDERFemale 3.60147 36.65189 1.72726 2.085 0.0371 *
## age:PTGENDERFemale -0.05243 0.94892 0.02477 -2.117 0.0343 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_PGSHigh PGS 0.9843 1.01599 0.7543 1.2843
## thirtile_PGSLow PGS 1.2417 0.80536 0.9715 1.5870
## age 0.9756 1.02503 0.9438 1.0085
## PTGENDERFemale 36.6519 0.02728 1.2412 1082.2776
## age:PTGENDERFemale 0.9489 1.05383 0.9040 0.9961
##
## Concordance= 0.563 (se = 0.017 )
## Likelihood ratio test= 23.26 on 5 df, p=3e-04
## Wald test = 23.46 on 5 df, p=3e-04
## Score (logrank) test = 23.62 on 5 df, p=3e-04## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_years +
## age + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 2694, number of events= 368
## (1943 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_yearsLow EA 0.48234 1.61986 0.12937 3.728 0.000193 ***
## thirtile_yearsHigh EA 0.08913 1.09322 0.13013 0.685 0.493397
## age -0.03138 0.96911 0.01692 -1.855 0.063609 .
## PTGENDERFemale 3.59276 36.33417 1.72457 2.083 0.037226 *
## age:PTGENDERFemale -0.05310 0.94828 0.02473 -2.148 0.031737 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_yearsLow EA 1.6199 0.61734 1.2571 2.0874
## thirtile_yearsHigh EA 1.0932 0.91473 0.8471 1.4108
## age 0.9691 1.03188 0.9375 1.0018
## PTGENDERFemale 36.3342 0.02752 1.2370 1067.2709
## age:PTGENDERFemale 0.9483 1.05454 0.9034 0.9954
##
## Concordance= 0.581 (se = 0.016 )
## Likelihood ratio test= 34.31 on 5 df, p=2e-06
## Wald test = 35.12 on 5 df, p=1e-06
## Score (logrank) test = 35.24 on 5 df, p=1e-06
EcogPt Everyday Memory
## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_PGS +
## age + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 2694, number of events= 515
## (1943 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_PGSHigh PGS -0.04258 0.95832 0.11386 -0.374 0.7085
## thirtile_PGSLow PGS 0.14803 1.15955 0.10604 1.396 0.1627
## age -0.03334 0.96721 0.01451 -2.298 0.0216 *
## PTGENDERFemale 6.50247 666.78878 1.46033 4.453 8.48e-06 ***
## age:PTGENDERFemale -0.09415 0.91014 0.02108 -4.467 7.92e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_PGSHigh PGS 0.9583 1.0435 0.7666 1.198e+00
## thirtile_PGSLow PGS 1.1595 0.8624 0.9420 1.427e+00
## age 0.9672 1.0339 0.9401 9.951e-01
## PTGENDERFemale 666.7888 0.0015 38.1026 1.167e+04
## age:PTGENDERFemale 0.9101 1.0987 0.8733 9.485e-01
##
## Concordance= 0.597 (se = 0.014 )
## Likelihood ratio test= 71.47 on 5 df, p=5e-14
## Wald test = 73.09 on 5 df, p=2e-14
## Score (logrank) test = 74.19 on 5 df, p=1e-14## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_years +
## age + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 2694, number of events= 515
## (1943 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_yearsLow EA 0.25453 1.28985 0.11286 2.255 0.02411 *
## thirtile_yearsHigh EA 0.15906 1.17241 0.10548 1.508 0.13157
## age -0.03752 0.96317 0.01451 -2.585 0.00973 **
## PTGENDERFemale 6.32092 556.08486 1.46005 4.329 1.50e-05 ***
## age:PTGENDERFemale -0.09184 0.91225 0.02107 -4.359 1.31e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_yearsLow EA 1.2898 0.775285 1.0339 1.6092
## thirtile_yearsHigh EA 1.1724 0.852941 0.9534 1.4417
## age 0.9632 1.038236 0.9362 0.9910
## PTGENDERFemale 556.0849 0.001798 31.7942 9726.0060
## age:PTGENDERFemale 0.9122 1.096195 0.8753 0.9507
##
## Concordance= 0.6 (se = 0.014 )
## Likelihood ratio test= 73.23 on 5 df, p=2e-14
## Wald test = 75.08 on 5 df, p=9e-15
## Score (logrank) test = 75.96 on 5 df, p=6e-15
EcogPt Everyday Organization
## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_PGS +
## age + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 2659, number of events= 429
## (1978 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_PGSHigh PGS -0.14839 0.86210 0.12252 -1.211 0.22585
## thirtile_PGSLow PGS -0.01112 0.98894 0.11494 -0.097 0.92289
## age -0.02381 0.97647 0.01558 -1.528 0.12645
## PTGENDERFemale 4.72134 112.31889 1.61805 2.918 0.00352 **
## age:PTGENDERFemale -0.07020 0.93221 0.02326 -3.018 0.00254 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_PGSHigh PGS 0.8621 1.159962 0.6781 1.0961
## thirtile_PGSLow PGS 0.9889 1.011187 0.7895 1.2388
## age 0.9765 1.024101 0.9471 1.0067
## PTGENDERFemale 112.3189 0.008903 4.7115 2677.5837
## age:PTGENDERFemale 0.9322 1.072720 0.8907 0.9757
##
## Concordance= 0.576 (se = 0.015 )
## Likelihood ratio test= 30.5 on 5 df, p=1e-05
## Wald test = 29.27 on 5 df, p=2e-05
## Score (logrank) test = 29.52 on 5 df, p=2e-05## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_years +
## age + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 2659, number of events= 429
## (1978 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_yearsLow EA 0.16495 1.17934 0.12623 1.307 0.19130
## thirtile_yearsHigh EA 0.22208 1.24867 0.11380 1.952 0.05099 .
## age -0.02556 0.97477 0.01557 -1.642 0.10060
## PTGENDERFemale 4.42736 83.70984 1.62143 2.731 0.00632 **
## age:PTGENDERFemale -0.06611 0.93603 0.02331 -2.837 0.00456 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_yearsLow EA 1.1793 0.84793 0.9209 1.5104
## thirtile_yearsHigh EA 1.2487 0.80085 0.9990 1.5607
## age 0.9748 1.02589 0.9455 1.0050
## PTGENDERFemale 83.7098 0.01195 3.4883 2008.8290
## age:PTGENDERFemale 0.9360 1.06835 0.8942 0.9798
##
## Concordance= 0.586 (se = 0.016 )
## Likelihood ratio test= 32.72 on 5 df, p=4e-06
## Wald test = 31.47 on 5 df, p=8e-06
## Score (logrank) test = 31.76 on 5 df, p=7e-06
EcogPt Everyday Planning
## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_PGS +
## age + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 2693, number of events= 605
## (1944 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_PGSHigh PGS -1.277e-01 8.801e-01 1.043e-01 -1.225 0.220
## thirtile_PGSLow PGS 9.631e-02 1.101e+00 9.670e-02 0.996 0.319
## age 8.555e-03 1.009e+00 1.282e-02 0.668 0.504
## PTGENDERFemale 7.794e+00 2.425e+03 1.399e+00 5.569 2.56e-08 ***
## age:PTGENDERFemale -1.173e-01 8.893e-01 2.013e-02 -5.828 5.62e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_PGSHigh PGS 0.8801 1.1362661 0.7174 1.080e+00
## thirtile_PGSLow PGS 1.1011 0.9081796 0.9110 1.331e+00
## age 1.0086 0.9914818 0.9836 1.034e+00
## PTGENDERFemale 2425.0524 0.0004124 156.1571 3.766e+04
## age:PTGENDERFemale 0.8893 1.1244370 0.8549 9.251e-01
##
## Concordance= 0.604 (se = 0.012 )
## Likelihood ratio test= 67.96 on 5 df, p=3e-13
## Wald test = 59.07 on 5 df, p=2e-11
## Score (logrank) test = 60.77 on 5 df, p=8e-12## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_years +
## age + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 2693, number of events= 605
## (1944 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_yearsLow EA 2.297e-03 1.002e+00 1.032e-01 0.022 0.982
## thirtile_yearsHigh EA -8.956e-02 9.143e-01 9.534e-02 -0.939 0.348
## age 7.579e-03 1.008e+00 1.285e-02 0.590 0.555
## PTGENDERFemale 7.791e+00 2.419e+03 1.397e+00 5.577 2.45e-08 ***
## age:PTGENDERFemale -1.172e-01 8.894e-01 2.009e-02 -5.834 5.42e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_yearsLow EA 1.0023 0.9977055 0.8187 1.227e+00
## thirtile_yearsHigh EA 0.9143 1.0936953 0.7585 1.102e+00
## age 1.0076 0.9924492 0.9826 1.033e+00
## PTGENDERFemale 2419.3506 0.0004133 156.5187 3.740e+04
## age:PTGENDERFemale 0.8894 1.1243324 0.8551 9.251e-01
##
## Concordance= 0.592 (se = 0.012 )
## Likelihood ratio test= 64.16 on 5 df, p=2e-12
## Wald test = 55.71 on 5 df, p=9e-11
## Score (logrank) test = 57.27 on 5 df, p=4e-11
EcogPt Everyday Visuospatial Abilities
## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_PGS +
## age + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 2687, number of events= 452
## (1950 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_PGSHigh PGS 0.06251 1.06450 0.11677 0.535 0.592
## thirtile_PGSLow PGS -0.02126 0.97897 0.11530 -0.184 0.854
## age 0.02436 1.02465 0.01717 1.418 0.156
## PTGENDERFemale 8.10367 3306.56934 1.60365 5.053 4.34e-07 ***
## age:PTGENDERFemale -0.11146 0.89453 0.02282 -4.884 1.04e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_PGSHigh PGS 1.0645 0.9394083 0.8467 1.338e+00
## thirtile_PGSLow PGS 0.9790 1.0214830 0.7810 1.227e+00
## age 1.0247 0.9759391 0.9907 1.060e+00
## PTGENDERFemale 3306.5693 0.0003024 142.6762 7.663e+04
## age:PTGENDERFemale 0.8945 1.1179037 0.8554 9.354e-01
##
## Concordance= 0.589 (se = 0.015 )
## Likelihood ratio test= 44.22 on 5 df, p=2e-08
## Wald test = 47.92 on 5 df, p=4e-09
## Score (logrank) test = 49.24 on 5 df, p=2e-09## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_years +
## age + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 2687, number of events= 452
## (1950 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_yearsLow EA 0.28744 1.33302 0.11703 2.456 0.014 *
## thirtile_yearsHigh EA 0.04390 1.04488 0.11501 0.382 0.703
## age 0.02204 1.02229 0.01711 1.288 0.198
## PTGENDERFemale 8.19504 3622.94392 1.60278 5.113 3.17e-07 ***
## age:PTGENDERFemale -0.11329 0.89289 0.02281 -4.967 6.81e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_yearsLow EA 1.3330 0.750179 1.0598 1.677e+00
## thirtile_yearsHigh EA 1.0449 0.957047 0.8340 1.309e+00
## age 1.0223 0.978197 0.9886 1.057e+00
## PTGENDERFemale 3622.9439 0.000276 156.5941 8.382e+04
## age:PTGENDERFemale 0.8929 1.119956 0.8539 9.337e-01
##
## Concordance= 0.588 (se = 0.015 )
## Likelihood ratio test= 50.43 on 5 df, p=1e-09
## Wald test = 54.39 on 5 df, p=2e-10
## Score (logrank) test = 55.7 on 5 df, p=9e-11
EcogPt Total
## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_PGS +
## age + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 2694, number of events= 495
## (1943 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_PGSHigh PGS 1.220e-01 1.130e+00 1.120e-01 1.089 0.276
## thirtile_PGSLow PGS 3.657e-02 1.037e+00 1.107e-01 0.330 0.741
## age -3.383e-03 9.966e-01 1.545e-02 -0.219 0.827
## PTGENDERFemale 7.417e+00 1.664e+03 1.500e+00 4.946 7.57e-07 ***
## age:PTGENDERFemale -1.050e-01 9.003e-01 2.150e-02 -4.883 1.05e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_PGSHigh PGS 1.1297 0.8851706 0.9070 1.407e+00
## thirtile_PGSLow PGS 1.0372 0.9640892 0.8350 1.289e+00
## age 0.9966 1.0033886 0.9669 1.027e+00
## PTGENDERFemale 1664.4745 0.0006008 88.0684 3.146e+04
## age:PTGENDERFemale 0.9003 1.1106819 0.8632 9.391e-01
##
## Concordance= 0.573 (se = 0.015 )
## Likelihood ratio test= 51.07 on 5 df, p=8e-10
## Wald test = 54.83 on 5 df, p=1e-10
## Score (logrank) test = 55.57 on 5 df, p=1e-10## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_years +
## age + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 2694, number of events= 495
## (1943 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_yearsLow EA 3.033e-01 1.354e+00 1.136e-01 2.670 0.00759 **
## thirtile_yearsHigh EA 1.232e-01 1.131e+00 1.091e-01 1.129 0.25896
## age -6.293e-03 9.937e-01 1.542e-02 -0.408 0.68309
## PTGENDERFemale 7.411e+00 1.653e+03 1.500e+00 4.941 7.77e-07 ***
## age:PTGENDERFemale -1.054e-01 8.999e-01 2.150e-02 -4.904 9.40e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_yearsLow EA 1.3543 0.7383784 1.0840 1.692e+00
## thirtile_yearsHigh EA 1.1311 0.8840967 0.9133 1.401e+00
## age 0.9937 1.0063133 0.9642 1.024e+00
## PTGENDERFemale 1653.3556 0.0006048 87.4503 3.126e+04
## age:PTGENDERFemale 0.8999 1.1111831 0.8628 9.387e-01
##
## Concordance= 0.592 (se = 0.014 )
## Likelihood ratio test= 56.94 on 5 df, p=5e-11
## Wald test = 60.84 on 5 df, p=8e-12
## Score (logrank) test = 61.46 on 5 df, p=6e-12
Self-Reported Everyday Cognitive Abilities Questionnaire (EcogSP)
EcogSP Everyday Divided Attention
## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_PGS +
## age + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 2665, number of events= 659
## (1972 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_PGSHigh PGS -0.332919 0.716828 0.104872 -3.175 0.0015 **
## thirtile_PGSLow PGS 0.213157 1.237579 0.089907 2.371 0.0177 *
## age -0.007643 0.992387 0.011650 -0.656 0.5118
## PTGENDERFemale 0.955143 2.599043 1.377250 0.694 0.4880
## age:PTGENDERFemale -0.022399 0.977850 0.019665 -1.139 0.2547
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_PGSHigh PGS 0.7168 1.3950 0.5836 0.8804
## thirtile_PGSLow PGS 1.2376 0.8080 1.0376 1.4761
## age 0.9924 1.0077 0.9700 1.0153
## PTGENDERFemale 2.5990 0.3848 0.1748 38.6484
## age:PTGENDERFemale 0.9778 1.0227 0.9409 1.0163
##
## Concordance= 0.619 (se = 0.013 )
## Likelihood ratio test= 86.21 on 5 df, p=<2e-16
## Wald test = 81.61 on 5 df, p=4e-16
## Score (logrank) test = 83.78 on 5 df, p=<2e-16## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_years +
## age + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 2665, number of events= 659
## (1972 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_yearsLow EA -0.029669 0.970767 0.097154 -0.305 0.760
## thirtile_yearsHigh EA -0.227095 0.796845 0.092405 -2.458 0.014 *
## age -0.008749 0.991289 0.011680 -0.749 0.454
## PTGENDERFemale 1.078686 2.940812 1.371412 0.787 0.432
## age:PTGENDERFemale -0.023967 0.976318 0.019583 -1.224 0.221
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_yearsLow EA 0.9708 1.030 0.8024 1.1744
## thirtile_yearsHigh EA 0.7968 1.255 0.6648 0.9551
## age 0.9913 1.009 0.9689 1.0142
## PTGENDERFemale 2.9408 0.340 0.2000 43.2330
## age:PTGENDERFemale 0.9763 1.024 0.9396 1.0145
##
## Concordance= 0.597 (se = 0.013 )
## Likelihood ratio test= 62.35 on 5 df, p=4e-12
## Wald test = 58.79 on 5 df, p=2e-11
## Score (logrank) test = 60.54 on 5 df, p=9e-12
EcogSP Everyday Language
## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_PGS +
## age + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 2689, number of events= 844
## (1948 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_PGSHigh PGS -0.183411 0.832426 0.090404 -2.029 0.04248 *
## thirtile_PGSLow PGS 0.215330 1.240271 0.080719 2.668 0.00764 **
## age -0.011966 0.988105 0.010633 -1.125 0.26040
## PTGENDERFemale -0.186897 0.829529 1.193688 -0.157 0.87558
## age:PTGENDERFemale -0.002649 0.997354 0.017007 -0.156 0.87621
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_PGSHigh PGS 0.8324 1.2013 0.69726 0.9938
## thirtile_PGSLow PGS 1.2403 0.8063 1.05879 1.4529
## age 0.9881 1.0120 0.96773 1.0089
## PTGENDERFemale 0.8295 1.2055 0.07994 8.6080
## age:PTGENDERFemale 0.9974 1.0027 0.96466 1.0312
##
## Concordance= 0.577 (se = 0.012 )
## Likelihood ratio test= 49.34 on 5 df, p=2e-09
## Wald test = 48.75 on 5 df, p=3e-09
## Score (logrank) test = 49.25 on 5 df, p=2e-09## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_years +
## age + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 2689, number of events= 844
## (1948 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_yearsLow EA 0.218800 1.244583 0.084362 2.594 0.0095 **
## thirtile_yearsHigh EA -0.138007 0.871092 0.083737 -1.648 0.0993 .
## age -0.014896 0.985215 0.010662 -1.397 0.1624
## PTGENDERFemale 0.086878 1.090764 1.192712 0.073 0.9419
## age:PTGENDERFemale -0.006918 0.993105 0.016997 -0.407 0.6840
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_yearsLow EA 1.2446 0.8035 1.0549 1.468
## thirtile_yearsHigh EA 0.8711 1.1480 0.7392 1.026
## age 0.9852 1.0150 0.9648 1.006
## PTGENDERFemale 1.0908 0.9168 0.1053 11.297
## age:PTGENDERFemale 0.9931 1.0069 0.9606 1.027
##
## Concordance= 0.578 (se = 0.012 )
## Likelihood ratio test= 43.79 on 5 df, p=3e-08
## Wald test = 43.31 on 5 df, p=3e-08
## Score (logrank) test = 43.67 on 5 df, p=3e-08
EcogSP Everyday Memory
## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_PGS +
## age + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 2689, number of events= 830
## (1948 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_PGSHigh PGS -0.21433 0.80708 0.09127 -2.348 0.0189 *
## thirtile_PGSLow PGS 0.20791 1.23110 0.08104 2.566 0.0103 *
## age -0.01180 0.98827 0.01061 -1.113 0.2658
## PTGENDERFemale -1.37132 0.25377 1.23187 -1.113 0.2656
## age:PTGENDERFemale 0.01239 1.01247 0.01754 0.706 0.4799
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_PGSHigh PGS 0.8071 1.2390 0.67489 0.9652
## thirtile_PGSLow PGS 1.2311 0.8123 1.05030 1.4430
## age 0.9883 1.0119 0.96794 1.0090
## PTGENDERFemale 0.2538 3.9406 0.02269 2.8380
## age:PTGENDERFemale 1.0125 0.9877 0.97825 1.0479
##
## Concordance= 0.597 (se = 0.012 )
## Likelihood ratio test= 71.01 on 5 df, p=6e-14
## Wald test = 69.42 on 5 df, p=1e-13
## Score (logrank) test = 70.55 on 5 df, p=8e-14## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_years +
## age + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 2689, number of events= 830
## (1948 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_yearsLow EA 0.040412 1.041240 0.087043 0.464 0.642
## thirtile_yearsHigh EA -0.107939 0.897682 0.082280 -1.312 0.190
## age -0.012334 0.987742 0.010636 -1.160 0.246
## PTGENDERFemale -1.181493 0.306820 1.229927 -0.961 0.337
## age:PTGENDERFemale 0.009738 1.009785 0.017520 0.556 0.578
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_yearsLow EA 1.0412 0.9604 0.87793 1.235
## thirtile_yearsHigh EA 0.8977 1.1140 0.76399 1.055
## age 0.9877 1.0124 0.96736 1.009
## PTGENDERFemale 0.3068 3.2592 0.02754 3.418
## age:PTGENDERFemale 1.0098 0.9903 0.97570 1.045
##
## Concordance= 0.582 (se = 0.012 )
## Likelihood ratio test= 50.3 on 5 df, p=1e-09
## Wald test = 48.94 on 5 df, p=2e-09
## Score (logrank) test = 49.87 on 5 df, p=1e-09
EcogSP Everyday Organization
## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_PGS +
## age + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 2607, number of events= 579
## (2030 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_PGSHigh PGS -0.276207 0.758656 0.112279 -2.460 0.01389 *
## thirtile_PGSLow PGS 0.270969 1.311235 0.096235 2.816 0.00487 **
## age -0.004967 0.995045 0.012717 -0.391 0.69609
## PTGENDERFemale -0.867910 0.419828 1.480417 -0.586 0.55770
## age:PTGENDERFemale 0.004700 1.004712 0.021053 0.223 0.82333
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_PGSHigh PGS 0.7587 1.3181 0.60880 0.9454
## thirtile_PGSLow PGS 1.3112 0.7626 1.08584 1.5834
## age 0.9950 1.0050 0.97055 1.0202
## PTGENDERFemale 0.4198 2.3819 0.02306 7.6420
## age:PTGENDERFemale 1.0047 0.9953 0.96410 1.0470
##
## Concordance= 0.598 (se = 0.013 )
## Likelihood ratio test= 63.97 on 5 df, p=2e-12
## Wald test = 62.18 on 5 df, p=4e-12
## Score (logrank) test = 63.35 on 5 df, p=2e-12## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_years +
## age + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 2607, number of events= 579
## (2030 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_yearsLow EA -0.050755 0.950512 0.101884 -0.498 0.61837
## thirtile_yearsHigh EA -0.377246 0.685747 0.100317 -3.761 0.00017 ***
## age -0.005946 0.994072 0.012746 -0.467 0.64086
## PTGENDERFemale -0.396545 0.672640 1.476794 -0.269 0.78830
## age:PTGENDERFemale -0.001912 0.998090 0.021006 -0.091 0.92749
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_yearsLow EA 0.9505 1.052 0.77846 1.1606
## thirtile_yearsHigh EA 0.6857 1.458 0.56334 0.8347
## age 0.9941 1.006 0.96955 1.0192
## PTGENDERFemale 0.6726 1.487 0.03722 12.1572
## age:PTGENDERFemale 0.9981 1.002 0.95783 1.0400
##
## Concordance= 0.596 (se = 0.014 )
## Likelihood ratio test= 52.48 on 5 df, p=4e-10
## Wald test = 50.75 on 5 df, p=1e-09
## Score (logrank) test = 51.7 on 5 df, p=6e-10
EcogSP Everyday Planning
## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_PGS +
## age + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 2672, number of events= 769
## (1965 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_PGSHigh PGS -0.2855199 0.7516234 0.0966275 -2.955 0.00313 **
## thirtile_PGSLow PGS 0.2629865 1.3008092 0.0835625 3.147 0.00165 **
## age -0.0111178 0.9889438 0.0109967 -1.011 0.31201
## PTGENDERFemale -0.5085910 0.6013423 1.2696078 -0.401 0.68872
## age:PTGENDERFemale -0.0001848 0.9998152 0.0180859 -0.010 0.99185
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_PGSHigh PGS 0.7516 1.3305 0.62194 0.9083
## thirtile_PGSLow PGS 1.3008 0.7688 1.10429 1.5323
## age 0.9889 1.0112 0.96786 1.0105
## PTGENDERFemale 0.6013 1.6629 0.04994 7.2413
## age:PTGENDERFemale 0.9998 1.0002 0.96499 1.0359
##
## Concordance= 0.601 (se = 0.012 )
## Likelihood ratio test= 81.98 on 5 df, p=3e-16
## Wald test = 79.77 on 5 df, p=9e-16
## Score (logrank) test = 81.23 on 5 df, p=5e-16## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_years +
## age + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 2672, number of events= 769
## (1965 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_yearsLow EA -0.037801 0.962904 0.087044 -0.434 0.664
## thirtile_yearsHigh EA -0.488919 0.613289 0.088622 -5.517 3.45e-08 ***
## age -0.011583 0.988483 0.011017 -1.051 0.293
## PTGENDERFemale 0.046360 1.047451 1.262406 0.037 0.971
## age:PTGENDERFemale -0.008059 0.991974 0.017986 -0.448 0.654
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_yearsLow EA 0.9629 1.0385 0.81188 1.1420
## thirtile_yearsHigh EA 0.6133 1.6306 0.51550 0.7296
## age 0.9885 1.0117 0.96737 1.0101
## PTGENDERFemale 1.0475 0.9547 0.08822 12.4364
## age:PTGENDERFemale 0.9920 1.0081 0.95761 1.0276
##
## Concordance= 0.602 (se = 0.012 )
## Likelihood ratio test= 81.38 on 5 df, p=4e-16
## Wald test = 78.26 on 5 df, p=2e-15
## Score (logrank) test = 79.79 on 5 df, p=9e-16
EcogSP Everyday Visuospatial Abilities
## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_PGS +
## age + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 2672, number of events= 701
## (1965 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_PGSHigh PGS -0.121675 0.885436 0.100070 -1.216 0.224022
## thirtile_PGSLow PGS 0.294408 1.342331 0.089010 3.308 0.000941 ***
## age -0.009391 0.990653 0.012036 -0.780 0.435222
## PTGENDERFemale 0.803065 2.232372 1.291602 0.622 0.534100
## age:PTGENDERFemale -0.014298 0.985804 0.018411 -0.777 0.437387
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_PGSHigh PGS 0.8854 1.129 0.7277 1.077
## thirtile_PGSLow PGS 1.3423 0.745 1.1274 1.598
## age 0.9907 1.009 0.9676 1.014
## PTGENDERFemale 2.2324 0.448 0.1776 28.066
## age:PTGENDERFemale 0.9858 1.014 0.9509 1.022
##
## Concordance= 0.567 (se = 0.013 )
## Likelihood ratio test= 30.75 on 5 df, p=1e-05
## Wald test = 30.79 on 5 df, p=1e-05
## Score (logrank) test = 31.08 on 5 df, p=9e-06## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_years +
## age + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 2672, number of events= 701
## (1965 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_yearsLow EA 0.24293 1.27498 0.09153 2.654 0.00795 **
## thirtile_yearsHigh EA -0.18658 0.82979 0.09326 -2.001 0.04543 *
## age -0.01312 0.98697 0.01207 -1.087 0.27686
## PTGENDERFemale 1.20280 3.32942 1.29010 0.932 0.35117
## age:PTGENDERFemale -0.02039 0.97982 0.01839 -1.109 0.26756
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_yearsLow EA 1.2750 0.7843 1.0656 1.5255
## thirtile_yearsHigh EA 0.8298 1.2051 0.6912 0.9962
## age 0.9870 1.0132 0.9639 1.0106
## PTGENDERFemale 3.3294 0.3004 0.2656 41.7353
## age:PTGENDERFemale 0.9798 1.0206 0.9451 1.0158
##
## Concordance= 0.559 (se = 0.013 )
## Likelihood ratio test= 28.66 on 5 df, p=3e-05
## Wald test = 28.48 on 5 df, p=3e-05
## Score (logrank) test = 28.64 on 5 df, p=3e-05
EcogSP Total
## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_PGS +
## age + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 2681, number of events= 850
## (1956 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_PGSHigh PGS -0.283336 0.753267 0.090937 -3.116 0.00183 **
## thirtile_PGSLow PGS 0.211630 1.235690 0.079496 2.662 0.00776 **
## age 0.006763 1.006786 0.010696 0.632 0.52719
## PTGENDERFemale 0.704794 2.023430 1.214431 0.580 0.56168
## age:PTGENDERFemale -0.016663 0.983476 0.017283 -0.964 0.33501
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_PGSHigh PGS 0.7533 1.3276 0.6303 0.9002
## thirtile_PGSLow PGS 1.2357 0.8093 1.0574 1.4440
## age 1.0068 0.9933 0.9859 1.0281
## PTGENDERFemale 2.0234 0.4942 0.1872 21.8682
## age:PTGENDERFemale 0.9835 1.0168 0.9507 1.0174
##
## Concordance= 0.6 (se = 0.012 )
## Likelihood ratio test= 74.43 on 5 df, p=1e-14
## Wald test = 72.28 on 5 df, p=3e-14
## Score (logrank) test = 73.38 on 5 df, p=2e-14## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_years +
## age + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 2681, number of events= 850
## (1956 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_yearsLow EA 0.066539 1.068802 0.083986 0.792 0.42821
## thirtile_yearsHigh EA -0.251932 0.777298 0.082833 -3.041 0.00235 **
## age 0.005981 1.005999 0.010730 0.557 0.57725
## PTGENDERFemale 1.058543 2.882170 1.211342 0.874 0.38219
## age:PTGENDERFemale -0.021745 0.978489 0.017243 -1.261 0.20728
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_yearsLow EA 1.0688 0.9356 0.9066 1.2600
## thirtile_yearsHigh EA 0.7773 1.2865 0.6608 0.9143
## age 1.0060 0.9940 0.9851 1.0274
## PTGENDERFemale 2.8822 0.3470 0.2683 30.9611
## age:PTGENDERFemale 0.9785 1.0220 0.9460 1.0121
##
## Concordance= 0.584 (se = 0.012 )
## Likelihood ratio test= 56.58 on 5 df, p=6e-11
## Wald test = 54.88 on 5 df, p=1e-10
## Score (logrank) test = 55.65 on 5 df, p=1e-10
Print session info:
## R version 4.3.0 (2023-04-21 ucrt)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 11 x64 (build 22621)
##
## Matrix products: default
##
##
## locale:
## [1] LC_COLLATE=English_Belgium.utf8 LC_CTYPE=English_Belgium.utf8
## [3] LC_MONETARY=English_Belgium.utf8 LC_NUMERIC=C
## [5] LC_TIME=English_Belgium.utf8
##
## time zone: Europe/Brussels
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] survminer_0.4.9 ggpubr_0.6.0 survival_3.5-5 ggplot2_3.4.2
## [5] rmdformats_1.0.4 gridExtra_2.3 NormPsy_1.0.8 dplyr_1.1.2
##
## loaded via a namespace (and not attached):
## [1] gtable_0.3.3 xfun_0.39 bslib_0.5.0 rstatix_0.7.2
## [5] lattice_0.21-8 vctrs_0.6.2 tools_4.3.0 generics_0.1.3
## [9] parallel_4.3.0 tibble_3.2.1 fansi_1.0.4 highr_0.10
## [13] pkgconfig_2.0.3 Matrix_1.6-1.1 data.table_1.14.8 lifecycle_1.0.3
## [17] farver_2.1.1 compiler_4.3.0 munsell_0.5.0 codetools_0.2-19
## [21] carData_3.0-5 htmltools_0.5.5 sass_0.4.6 yaml_2.3.7
## [25] pillar_1.9.0 car_3.1-2 jquerylib_0.1.4 tidyr_1.3.0
## [29] randtoolbox_2.0.4 cachem_1.0.8 iterators_1.0.14 abind_1.4-5
## [33] foreach_1.5.2 nlme_3.1-162 km.ci_0.5-6 lcmm_2.0.2
## [37] tidyselect_1.2.0 digest_0.6.31 mvtnorm_1.2-2 purrr_1.0.1
## [41] bookdown_0.35 labeling_0.4.2 splines_4.3.0 fastmap_1.1.1
## [45] grid_4.3.0 colorspace_2.1-0 cli_3.6.1 magrittr_2.0.3
## [49] utf8_1.2.3 broom_1.0.5 withr_2.5.0 scales_1.2.1
## [53] backports_1.4.1 rmarkdown_2.22 rngWELL_0.10-9 ggsignif_0.6.4
## [57] zoo_1.8-12 evaluate_0.21 knitr_1.43 KMsurv_0.1-5
## [61] doParallel_1.0.17 mgcv_1.8-42 survMisc_0.5.6 rlang_1.1.1
## [65] xtable_1.8-4 glue_1.6.2 rstudioapi_0.14 jsonlite_1.8.7
## [69] R6_2.5.1 marqLevAlg_2.0.8